-Abdel-Aty, M. A., and Abdelwahab, H. T., (2004). Predicting injury severity levels in traffic crashes: A modeling comparison. Journal of Transportation Engineering,
Vol. 130, No. 2, 204–210.
-Abellán, J., López, G., & de Oña, J. (2013). Analysis of traffic accident severity using decision rules via decision trees. Expert Systems with Applications, 40(15), 6047–6054.
-Amiri, A. M., Sadri, A., Nadimi, N., & Shams, M. (2020). A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed object crashes among elderly drivers. Accident Analysis and Prevention, 138, 105468.
-Anvari, M. B., Kashani, A. T., and Rabieyan, R. (2017). Identifying the most important factors in the at-fault probability of motorcyclists by data mining, based on classification tree models. International Journal of Civil Engineering, Vol. 15, No. 4, 653–662.
-Arhin, S. A., & Gatiba, A. (2020). Predicting crash injury severity at unsignalized intersections using support vector machines and naïve bayes classifiers. Transportation Safety and Environment, 2(2), 120– 132.
-Breiman, L. Random Forests (2001). Machine Learning, Vol. 45, No. 1, 5–32.
-Budiawan, W., Saptadi, S., Tjioe, C., & Phommachak, T. (2019). Traffic accident severity prediction using naive Bayes algorithm-a case study of Semarang toll road. IOP Conference Series: Materials Science and Engineering, 598, 012089.
-Cai, Q., M. Abdel-Aty, O. Zheng, and Y. Wu. (2022). Applying Machine Learning and Google Street View to Explore Effects of Drivers’ Visual Environment on Traffic Safety. Transportation Research Part C: Emerging Technologies, Vol. 135.
-Champahom, T., Se, CH., Watcharamaisakul, F., Jomnonkwao, S., Karoonsoontawong, A., Ratanavaraha, V., (2024). Tree-based approaches to understanding factors influencing crash severity across roadway classes: A Thailand case study, IATSS Research 48, 464–476.
-Champahom, T., Se, CH., Watcharamaisakul, F., Jomnonkwao, S., Karoonsoontawong, A., Ratanavaraha, V., (2024). Tree-based approaches to understanding factors influencing crash severity across roadway classes: A Thailand case study, IATSS Research 48, 464–476.
-Chen, C., Zhang, G. H., Yang, J. F., Milton, C. J., & Alcántara, A. D. (2016). An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier. Accident Analysis & Prevention, 90, 95–107.
-Chen, F., and Chen, S. (2011). Injury Severities of Truck Drivers in Single- and Multi-Vehicle Accidents on Rural Highways. Accident Analysis & Prevention, Vol. 43, No. 5,1677–1688.
-Chen, M.M. Chen, M.C., (2020). Modeling road accident severity with comparisons of logistic regression, decision tree and random forest, Inf. 11.
-Chen, M.M. Chen, M.C., (2020). Modeling road accident severity with comparisons of logistic regression, decision tree and random forest, Inf. 11.
-Chiou, Y. C., Fu, C. and Ke, C. Y. (2020). Modelling Two-Vehicle Crash Severity by Generalized Estimating Equations. Accident Analysis and Prevention, Vol. 148.
-Dong, N., Huang, H., & Zheng, L. (2015). Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects. Accident; Analysis and Prevention, 82, 192–198.
-Duncan, C. S., Khattak, A. J. and Council, F. M. (1998). Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions. Transportation Research Record: Journal of the Transportation Research Board, Vol. 1635, No. 1, 63–71.
-Fiorentini, N., & Losa, M. (2020). Handling imbalanced data in road crash severity prediction by machine learning algorithms. Infrastructures, 5(7), 61.
-Geedipally, S. R., and Lord, D. (2010). Investigating the Effect of Modeling Single Vehicle and Multi-Vehicle Crashes Separately on Confidence Intervals of Poisson–Gamma Models. Accident Analysis & Prevention, Vol. 42, No. 4, 1273–1282.
-Griffith, M. S. (1999). Safety Evaluation of Rolled-In Continuous Shoulder Rumble Strips Installed on Freeways. Transportation Research Record: Journal of the Transportation Research Board, 1665(1): 28–34.
-Guyon, I. and Elisseeff, A. (2003).An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157–82.
-Hagenauer, J., and Helbich, M. (2017). A Comparative Study of Machine Learning Classifiers for Modeling Travel Mode Choice. Expert Systems with Applications, Vol. 78, 273–282.
-Hossain, M. (2011). Development of a Real-Time Proactive Road Safety Management System for Urban Expressways. Ph. D. Thesis, Department of Built Environment, Tokyo Institute of Technology.
-Iranitalab, A., and Khattak, A. (2017). Comparison of four statistical and machine learning methods for crash severity prediction. Accident Analysis & Prevention, Vol. 108,
27–36.
-Ji, A., and Levinson, D. (2020). Injury Severity Prediction fom Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models. IEEE Open Journal of Intelligent Transportation Systems, Vol. 1, 217–226.
-Jiang, L., Xie, Y., and Ren, T. (2019). Modelling highly unbalanced crash injury severity data by ensemble methods and global sensitivity analysis. In Procceding Transportation Research Board 98th Annual Meeting, Washington, DC, USA, 13–17.
-Kitali, A. E., and Sando, T. (2017). A Full Bayesian Approach to Appraise the Safety Effects of Pedestrian Countdown Signals to Drivers. Accident Analysis & Prevention,
Vol. 106, 327–335.
-Lee, C., and Li, X. (2014). Analysis of Injury Severity of Drivers Involved in Single- and Two-Vehicle Crashes on Highways in Ontario. Accident Analysis and Prevention, Vol. 71, 286–295.
-Li, Z., Liu, P., Wang, W., & Xu, C. (2012). Using support vector machine models for crash injury severity analysis. Accident Analysis & Prevention, 45, 478 486.
-Li, Z., Liu, P., Wang, W., & Xu, C. (2012). Using support vector machine models for crash injury severity analysis. Accident Analysis & Prevention, 45, 478 486.
-Li, Z., Wang, H. X., Zhang, Y. W., and Zhao, X. H. (2020). Random Forest–Based Feature Selection and Detection Method for Drunk Driving Recognition. International Journal of Distributed Sensor Networks, Vol. 16, No. 2.
-Lombardi, D. A., Horrey, W. J., & Courtney, T. K. (2017). Age-related differences in fatal intersection crashes in the United States. Accident Analysis & Prevention, 99, 20–29.
-Lord, D., Washington, S. P., and Ivan, J. N. (2005). Poisson, Poisson-Gamma and Zero-Inflated Regression Models of Motor Vehicle Crashes: Balancing Statistical Fit and Theory. Accident Analysis & Prevention, Vol. 37, No. 1, 35–46.
-Lu, P., Zheng, Z., Ren, Y., Zhou, X., Keramati, A., Tolliver, D., & Huang, Y. (2020). A gradient boosting crash prediction approach for highway-rail grade crossing crash analysis. Journal of Advanced Transportation,1–10.
-Lundberg, S. M., and Lee, S. I. (2017).A Unified Approach to Interpreting Model Predictions.
-Ma, J., and Kockelman, K. M. (2006). Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity. Transportation Research Record: Journal of the Transportation Research Board, 1950(1):24–34.
-Ma, X., Chen, S., & Chen, F. (2016). Correlated Random-Effects Bivariate Poisson Lognormal Model to Study Single-Vehicle and Multivehicle Crashes. Journal of Transportation Engineering, 04016049.
-Martensen, H., and Dupont, E. (2013). Comparing Single Vehicle and Multivehicle Fatal Road Crashes: A Joint Analysis of Road Conditions, Time Variables and Driver Characteristics. Accident Analysis & Prevention, Vol. 60, 466–471.
-Mauro, R., De Luca, M., & Dell’Acqua, G. (2013). Using a K-means clustering algorithm to examine patterns of vehicle crashes in before-after analysis. Modern Applied Science, 7(10), 11.
-Mokhtarimousavi, S., Anderson, J. C., Azizinamini, A., & Hadi, M. (2019). Improved support vector machine models for work zone crash injury severity prediction and analysis. Transportation Research Record: Journal of the Transportation Research Board, 2673(11), 680–692.
-Mondal, A. R., Bhuiyan, M. A. E., & Yang, F. (2020). Advancement of weather related crash prediction model using nonparametric machine learning algorithms. SN Applied Sciences, 2(8), 1–11.
-NHTSA, (2020), Traffic Safety Facts, A Compilation of Motor Vehicle Crash Data, Report No. DOT HS 813375, National Highway Traffic Safety Administration, U.S, Department of Transportation.
-NHTSA, (2020), Traffic Safety Facts, A Compilation of Motor Vehicle Crash Data, Report No. DOT HS 813375, National Highway Traffic Safety Administration, U.S, Department of Transportation.
-Pasupathy, Ivan, R. J. N., and Ossen, P. J. (2000). Single and Multi-Vehicle Crash Prediction Models for Two-Lane Roadways. NEUTC UCNR9-8, Final Report, Durham, NH.
-Pradhan, B., and Sameen, M. I., (2020). Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach. In Advances in Science, Technology and Innovation, Springer Nature, 119–127.
-Qin, X., Ivan J. N., & Ravishanker, N. (2004). Selecting Exposure Measures in Crash Rate Prediction for Two-Lane Highway Segments. Accident Analysis & Prevention, Vol. 36, No. 2, 183–191.
-Sameen, M. I., & Pradhan, B. (2017). Severity Prediction of Traffic Accidents with Recurrent Neural Networks. Applied Sciences, 7(6), 476.
-Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2020). Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data. Safety Science, 125, 104616.
-Shao, X., Ma, X., Chen, F., Song, M., Pan, X., & You, K. (2020). A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions. International Journal of Environmental Research and Public Health, 17(2), 395.
-Sum, S., Se, Ch., Champahom, T., Jomnonkwao, S., Sinha, S., Ratanavaraha, V., (2025). A random forest and SHAP-based analysis of motorcycle crash severity in Thailand: Urban-rural and day-night perspectives, Transportation Engineering, 21, 100369.
-Tang, J., Liang, J., Han, C., Li, Z., & Huang, H. (2019). Crash injury severity analysis using a two-layer Stacking framework. Accident Analysis & Prevention, Vol. 122, 226-238.
-Wen, X., Xie, Y., Jiang, L., Pu, Z., & Ge, T. (2021). Applications of Machine Learning Methods in Traffic Crash Severity Modelling: Current Status and Future Directions. Transport Reviews, Vol. 41, No. 6, 855–879.
-World Health Organization (WHO), (2009). Global status report on road safety: Time for action.
-World Health Organization (WHO), (2018). World report on road traffic injury prevention.
-World Health Organization (WHO), (2022). Road Traffic Injuries.
-Yan, M., Shen, Y., (2022). Traffic accident severity prediction based on random forest, Sustain 14.
-Yan, M., Shen, Y., (2022). Traffic accident severity prediction based on random forest, Sustain 14.
-Yang, J., Ren, P., and Ando, R. (2019). Examining Drivers’ Injury Severity of Two Vehicle Crashes between Passenger Cars and Trucks Considering Vehicle Types. Asian Transport Studies, Vol. 5, Issue. 4, 720-733.
-Yassin, S. S., and Pooja (2020). Road Accident Prediction and Model Interpretation Using a Hybrid K-Means and Random Forest Algorithm Approach. SN Applied Sciences, Vol. 2, No. 9, 1–13.
-Yu, R., and Abdel-Aty, M. (2014). Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. Safety Science, Vol. 63, 50–56.
-Yuan, Q., Lu, M., Theofilatos, A., & Li, Y. B. (2017). Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China. Chinese Journal of Traumatology, 20(1), 20–26.
-Yuan, R., Gan, J., Peng, Z., and Xiang, Q. (2022). Injury Severity Analysis of Two Vehicle Crashes at Unsignalized Intersections Using Mixed Logit Models. International Journal of Injury Control and Safety Promotion.
-Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident Analysis and Prevention, 73, 351 358.
-Zeng, Q., Wen, H., and Huang, H. (2016). The Interactive Effect on Injury Severity of
Driver-Vehicle Units in Two-Vehicle Crashes. Journal of Safety Research, Vol. 59, 105–111.
-Zhang, J., Li, Z., Pu, Z., & Xu, C. (2018). Comparing prediction performance for crash injury severity among various machine learning and statistical methods. IEEE Access, 6,
60079–60087.
-Zhang, J., Li, Z., Pu, Z., & Xu, C. (2018). Comparing prediction performance for crash injury severity among various machine learning and statistical methods. IEEE Access, 6,
60079–60087.